from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
reporting = Reporting(config_file_path="config.yml")
reporting.run()
| hour | min | sec | |
|---|---|---|---|
| algo | |||
| KNeighborsClassifier | 0.0 | 13.0 | 39.784951 |
| daal4py_KNeighborsClassifier | 0.0 | 3.0 | 26.047757 |
| KNeighborsClassifier_kd_tree | 0.0 | 6.0 | 56.378027 |
| daal4py_KNeighborsClassifier_kd_tree | 0.0 | 1.0 | 45.585390 |
| KMeans_tall | 0.0 | 1.0 | 40.392747 |
| daal4py_KMeans_tall | 0.0 | 1.0 | 17.610193 |
| KMeans_short | 0.0 | 0.0 | 22.069937 |
| daal4py_KMeans_short | 0.0 | 0.0 | 9.368428 |
| LogisticRegression | 0.0 | 1.0 | 3.497449 |
| daal4py_LogisticRegression | 0.0 | 0.0 | 56.088611 |
| Ridge | 0.0 | 0.0 | 48.934829 |
| daal4py_Ridge | 0.0 | 0.0 | 15.630826 |
| total | 0.0 | 32.0 | 21.460815 |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | brute |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | algorithm | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier | fit | 0.143 | 0.002 | 1000000 | 1000000 | 100 | brute | -1 | 1 | 0.99 | 0.987 | 0.498 | 0.001 | 0.287 | 0.003 | See |
| 1 | KNeighborsClassifier | predict | 25.266 | 0.618 | 1000000 | 1000 | 100 | brute | -1 | 1 | 0.99 | 0.987 | 1.980 | 0.014 | 12.764 | 0.325 | See |
| 2 | KNeighborsClassifier | predict | 0.177 | 0.020 | 1000000 | 1 | 100 | brute | -1 | 1 | 0.99 | 0.987 | 0.085 | 0.000 | 2.074 | 0.236 | See |
| 3 | KNeighborsClassifier | fit | 0.139 | 0.003 | 1000000 | 1000000 | 100 | brute | -1 | 5 | 0.99 | 0.987 | 0.508 | 0.004 | 0.273 | 0.006 | See |
| 4 | KNeighborsClassifier | predict | 33.626 | 0.000 | 1000000 | 1000 | 100 | brute | -1 | 5 | 0.99 | 0.987 | 1.990 | 0.007 | 16.899 | 0.059 | See |
| 5 | KNeighborsClassifier | predict | 0.180 | 0.017 | 1000000 | 1 | 100 | brute | -1 | 5 | 0.99 | 0.987 | 0.085 | 0.000 | 2.122 | 0.201 | See |
| 6 | KNeighborsClassifier | fit | 0.143 | 0.002 | 1000000 | 1000000 | 100 | brute | -1 | 100 | 0.99 | 0.987 | 0.496 | 0.001 | 0.288 | 0.005 | See |
| 7 | KNeighborsClassifier | predict | 33.850 | 0.000 | 1000000 | 1000 | 100 | brute | -1 | 100 | 0.99 | 0.987 | 2.056 | 0.013 | 16.463 | 0.103 | See |
| 8 | KNeighborsClassifier | predict | 0.173 | 0.018 | 1000000 | 1 | 100 | brute | -1 | 100 | 0.99 | 0.987 | 0.085 | 0.000 | 2.040 | 0.210 | See |
| 9 | KNeighborsClassifier | fit | 0.143 | 0.003 | 1000000 | 1000000 | 100 | brute | 1 | 1 | 0.99 | 0.987 | 0.508 | 0.004 | 0.281 | 0.007 | See |
| 10 | KNeighborsClassifier | predict | 13.283 | 0.106 | 1000000 | 1000 | 100 | brute | 1 | 1 | 0.99 | 0.987 | 1.983 | 0.011 | 6.698 | 0.065 | See |
| 11 | KNeighborsClassifier | predict | 0.192 | 0.001 | 1000000 | 1 | 100 | brute | 1 | 1 | 0.99 | 0.987 | 0.085 | 0.000 | 2.260 | 0.015 | See |
| 12 | KNeighborsClassifier | fit | 0.144 | 0.003 | 1000000 | 1000000 | 100 | brute | 1 | 5 | 0.99 | 0.987 | 0.496 | 0.001 | 0.289 | 0.006 | See |
| 13 | KNeighborsClassifier | predict | 22.878 | 0.029 | 1000000 | 1000 | 100 | brute | 1 | 5 | 0.99 | 0.987 | 1.985 | 0.012 | 11.528 | 0.074 | See |
| 14 | KNeighborsClassifier | predict | 0.200 | 0.002 | 1000000 | 1 | 100 | brute | 1 | 5 | 0.99 | 0.987 | 0.085 | 0.001 | 2.357 | 0.028 | See |
| 15 | KNeighborsClassifier | fit | 0.143 | 0.002 | 1000000 | 1000000 | 100 | brute | 1 | 100 | 0.99 | 0.987 | 0.508 | 0.005 | 0.282 | 0.006 | See |
| 16 | KNeighborsClassifier | predict | 22.754 | 0.107 | 1000000 | 1000 | 100 | brute | 1 | 100 | 0.99 | 0.987 | 2.047 | 0.008 | 11.118 | 0.067 | See |
| 17 | KNeighborsClassifier | predict | 0.198 | 0.001 | 1000000 | 1 | 100 | brute | 1 | 100 | 0.99 | 0.987 | 0.085 | 0.001 | 2.330 | 0.016 | See |
| 18 | KNeighborsClassifier | fit | 0.060 | 0.001 | 1000000 | 1000000 | 2 | brute | -1 | 1 | 0.99 | 0.987 | 0.105 | 0.002 | 0.567 | 0.014 | See |
| 19 | KNeighborsClassifier | predict | 21.159 | 0.073 | 1000000 | 1000 | 2 | brute | -1 | 1 | 0.99 | 0.987 | 0.310 | 0.002 | 68.215 | 0.443 | See |
| 20 | KNeighborsClassifier | predict | 0.019 | 0.003 | 1000000 | 1 | 2 | brute | -1 | 1 | 0.99 | 0.987 | 0.006 | 0.001 | 3.123 | 0.640 | See |
| 21 | KNeighborsClassifier | fit | 0.060 | 0.001 | 1000000 | 1000000 | 2 | brute | -1 | 5 | 0.99 | 0.987 | 0.104 | 0.001 | 0.580 | 0.010 | See |
| 22 | KNeighborsClassifier | predict | 32.504 | 0.000 | 1000000 | 1000 | 2 | brute | -1 | 5 | 0.99 | 0.987 | 0.313 | 0.002 | 103.797 | 0.690 | See |
| 23 | KNeighborsClassifier | predict | 0.034 | 0.004 | 1000000 | 1 | 2 | brute | -1 | 5 | 0.99 | 0.987 | 0.006 | 0.001 | 5.523 | 0.936 | See |
| 24 | KNeighborsClassifier | fit | 0.059 | 0.001 | 1000000 | 1000000 | 2 | brute | -1 | 100 | 0.99 | 0.987 | 0.105 | 0.003 | 0.564 | 0.020 | See |
| 25 | KNeighborsClassifier | predict | 32.342 | 0.000 | 1000000 | 1000 | 2 | brute | -1 | 100 | 0.99 | 0.987 | 0.373 | 0.010 | 86.814 | 2.223 | See |
| 26 | KNeighborsClassifier | predict | 0.033 | 0.002 | 1000000 | 1 | 2 | brute | -1 | 100 | 0.99 | 0.987 | 0.006 | 0.001 | 5.163 | 0.908 | See |
| 27 | KNeighborsClassifier | fit | 0.071 | 0.002 | 1000000 | 1000000 | 2 | brute | 1 | 1 | 0.99 | 0.987 | 0.104 | 0.001 | 0.678 | 0.022 | See |
| 28 | KNeighborsClassifier | predict | 10.346 | 0.029 | 1000000 | 1000 | 2 | brute | 1 | 1 | 0.99 | 0.987 | 0.308 | 0.003 | 33.623 | 0.323 | See |
| 29 | KNeighborsClassifier | predict | 0.015 | 0.001 | 1000000 | 1 | 2 | brute | 1 | 1 | 0.99 | 0.987 | 0.007 | 0.001 | 2.215 | 0.312 | See |
| 30 | KNeighborsClassifier | fit | 0.070 | 0.002 | 1000000 | 1000000 | 2 | brute | 1 | 5 | 0.99 | 0.987 | 0.105 | 0.003 | 0.666 | 0.028 | See |
| 31 | KNeighborsClassifier | predict | 21.542 | 0.011 | 1000000 | 1000 | 2 | brute | 1 | 5 | 0.99 | 0.987 | 0.309 | 0.003 | 69.632 | 0.658 | See |
| 32 | KNeighborsClassifier | predict | 0.027 | 0.001 | 1000000 | 1 | 2 | brute | 1 | 5 | 0.99 | 0.987 | 0.006 | 0.001 | 4.324 | 0.814 | See |
| 33 | KNeighborsClassifier | fit | 0.070 | 0.002 | 1000000 | 1000000 | 2 | brute | 1 | 100 | 0.99 | 0.987 | 0.104 | 0.001 | 0.670 | 0.023 | See |
| 34 | KNeighborsClassifier | predict | 21.656 | 0.083 | 1000000 | 1000 | 2 | brute | 1 | 100 | 0.99 | 0.987 | 0.366 | 0.002 | 59.173 | 0.430 | See |
| 35 | KNeighborsClassifier | predict | 0.027 | 0.001 | 1000000 | 1 | 2 | brute | 1 | 100 | 0.99 | 0.987 | 0.006 | 0.000 | 4.450 | 0.370 | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | kd_tree |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | algorithm | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 2.966 | 0.052 | 1000000 | 1000000 | 10 | kd_tree | -1 | 1 | 0.99 | 0.985 | 0.726 | 0.013 | 4.087 | 0.101 | See |
| 1 | KNeighborsClassifier_kd_tree | predict | 0.461 | 0.002 | 1000000 | 1000 | 10 | kd_tree | -1 | 1 | 0.99 | 0.985 | 0.103 | 0.001 | 4.455 | 0.056 | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 0.006 | 0.005 | 1000000 | 1 | 10 | kd_tree | -1 | 1 | 0.99 | 0.985 | 0.000 | 0.000 | 13.392 | 11.622 | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 2.979 | 0.027 | 1000000 | 1000000 | 10 | kd_tree | -1 | 5 | 0.99 | 0.985 | 0.755 | 0.004 | 3.943 | 0.042 | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 0.878 | 0.004 | 1000000 | 1000 | 10 | kd_tree | -1 | 5 | 0.99 | 0.985 | 0.184 | 0.001 | 4.775 | 0.044 | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 0.004 | 0.001 | 1000000 | 1 | 10 | kd_tree | -1 | 5 | 0.99 | 0.985 | 0.001 | 0.000 | 6.563 | 2.735 | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 2.930 | 0.032 | 1000000 | 1000000 | 10 | kd_tree | -1 | 100 | 0.99 | 0.985 | 0.726 | 0.005 | 4.034 | 0.052 | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 2.862 | 0.010 | 1000000 | 1000 | 10 | kd_tree | -1 | 100 | 0.99 | 0.985 | 0.549 | 0.004 | 5.212 | 0.044 | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 0.006 | 0.001 | 1000000 | 1 | 10 | kd_tree | -1 | 100 | 0.99 | 0.985 | 0.001 | 0.000 | 5.037 | 1.966 | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 3.002 | 0.024 | 1000000 | 1000000 | 10 | kd_tree | 1 | 1 | 0.99 | 0.985 | 0.752 | 0.004 | 3.990 | 0.039 | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 0.742 | 0.005 | 1000000 | 1000 | 10 | kd_tree | 1 | 1 | 0.99 | 0.985 | 0.103 | 0.001 | 7.207 | 0.089 | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 10 | kd_tree | 1 | 1 | 0.99 | 0.985 | 0.000 | 0.000 | 2.723 | 1.175 | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 3.012 | 0.023 | 1000000 | 1000000 | 10 | kd_tree | 1 | 5 | 0.99 | 0.985 | 0.734 | 0.019 | 4.102 | 0.108 | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1.424 | 0.009 | 1000000 | 1000 | 10 | kd_tree | 1 | 5 | 0.99 | 0.985 | 0.183 | 0.001 | 7.784 | 0.065 | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 0.002 | 0.000 | 1000000 | 1 | 10 | kd_tree | 1 | 5 | 0.99 | 0.985 | 0.001 | 0.000 | 3.467 | 1.495 | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 3.006 | 0.023 | 1000000 | 1000000 | 10 | kd_tree | 1 | 100 | 0.99 | 0.985 | 0.751 | 0.004 | 4.001 | 0.036 | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 4.736 | 0.025 | 1000000 | 1000 | 10 | kd_tree | 1 | 100 | 0.99 | 0.985 | 0.552 | 0.002 | 8.586 | 0.056 | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 0.004 | 0.001 | 1000000 | 1 | 10 | kd_tree | 1 | 100 | 0.99 | 0.985 | 0.001 | 0.000 | 3.283 | 1.331 | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1.391 | 0.008 | 1000000 | 1000000 | 2 | kd_tree | -1 | 1 | 0.99 | 0.985 | 0.509 | 0.013 | 2.735 | 0.070 | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 0.030 | 0.001 | 1000000 | 1000 | 2 | kd_tree | -1 | 1 | 0.99 | 0.985 | 0.001 | 0.000 | 30.815 | 14.696 | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 0.002 | 0.000 | 1000000 | 1 | 2 | kd_tree | -1 | 1 | 0.99 | 0.985 | 0.000 | 0.000 | 19.684 | 14.346 | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1.405 | 0.008 | 1000000 | 1000000 | 2 | kd_tree | -1 | 5 | 0.99 | 0.985 | 0.498 | 0.004 | 2.819 | 0.025 | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 0.031 | 0.001 | 1000000 | 1000 | 2 | kd_tree | -1 | 5 | 0.99 | 0.985 | 0.001 | 0.001 | 22.428 | 9.768 | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 0.003 | 0.001 | 1000000 | 1 | 2 | kd_tree | -1 | 5 | 0.99 | 0.985 | 0.000 | 0.000 | 21.526 | 16.404 | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1.393 | 0.008 | 1000000 | 1000000 | 2 | kd_tree | -1 | 100 | 0.99 | 0.985 | 0.507 | 0.013 | 2.746 | 0.073 | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 0.049 | 0.001 | 1000000 | 1000 | 2 | kd_tree | -1 | 100 | 0.99 | 0.985 | 0.007 | 0.001 | 6.788 | 0.789 | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 0.002 | 0.000 | 1000000 | 1 | 2 | kd_tree | -1 | 100 | 0.99 | 0.985 | 0.000 | 0.000 | 19.393 | 13.409 | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1.389 | 0.008 | 1000000 | 1000000 | 2 | kd_tree | 1 | 1 | 0.99 | 0.985 | 0.510 | 0.011 | 2.725 | 0.062 | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 0.027 | 0.001 | 1000000 | 1000 | 2 | kd_tree | 1 | 1 | 0.99 | 0.985 | 0.001 | 0.000 | 22.038 | 6.808 | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 2 | kd_tree | 1 | 1 | 0.99 | 0.985 | 0.000 | 0.000 | 5.698 | 3.995 | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1.388 | 0.007 | 1000000 | 1000000 | 2 | kd_tree | 1 | 5 | 0.99 | 0.985 | 0.499 | 0.005 | 2.779 | 0.034 | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 0.030 | 0.001 | 1000000 | 1000 | 2 | kd_tree | 1 | 5 | 0.99 | 0.985 | 0.001 | 0.000 | 25.086 | 6.316 | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 2 | kd_tree | 1 | 5 | 0.99 | 0.985 | 0.000 | 0.000 | 5.663 | 3.753 | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1.396 | 0.009 | 1000000 | 1000000 | 2 | kd_tree | 1 | 100 | 0.99 | 0.985 | 0.501 | 0.006 | 2.783 | 0.037 | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 0.052 | 0.003 | 1000000 | 1000 | 2 | kd_tree | 1 | 100 | 0.99 | 0.985 | 0.008 | 0.002 | 6.566 | 1.410 | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 2 | kd_tree | 1 | 100 | 0.99 | 0.985 | 0.000 | 0.000 | 5.366 | 3.444 | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 3 |
| max_iter | 30 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | algorithm | init | max_iter | n_clusters | n_init | tol | n_iter_sklearn | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 0.638 | 0.007 | 1000000 | 1000000 | 2 | full | k-means++ | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.002 | 0.476 | 0.021 | 1.340 | 0.060 | See |
| 1 | KMeans_tall | predict | 0.000 | 0.000 | 1000000 | 1000 | 2 | full | k-means++ | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.002 | 0.000 | 0.000 | 1.999 | 1.218 | See |
| 2 | KMeans_tall | predict | 0.000 | 0.000 | 1000000 | 1 | 2 | full | k-means++ | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.002 | 0.000 | 0.000 | 2.271 | 1.796 | See |
| 3 | KMeans_tall | fit | 0.555 | 0.004 | 1000000 | 1000000 | 2 | full | random | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.002 | 0.428 | 0.017 | 1.296 | 0.052 | See |
| 4 | KMeans_tall | predict | 0.000 | 0.000 | 1000000 | 1000 | 2 | full | random | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.002 | 0.000 | 0.000 | 2.009 | 1.299 | See |
| 5 | KMeans_tall | predict | 0.000 | 0.000 | 1000000 | 1 | 2 | full | random | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.002 | 0.000 | 0.000 | 2.382 | 1.850 | See |
| 6 | KMeans_tall | fit | 6.574 | 0.087 | 1000000 | 1000000 | 100 | full | k-means++ | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.002 | 3.043 | 0.017 | 2.160 | 0.031 | See |
| 7 | KMeans_tall | predict | 0.001 | 0.000 | 1000000 | 1000 | 100 | full | k-means++ | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.002 | 0.000 | 0.000 | 2.157 | 1.065 | See |
| 8 | KMeans_tall | predict | 0.000 | 0.000 | 1000000 | 1 | 100 | full | k-means++ | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.002 | 0.000 | 0.000 | 1.964 | 1.356 | See |
| 9 | KMeans_tall | fit | 6.003 | 0.018 | 1000000 | 1000000 | 100 | full | random | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.002 | 2.875 | 0.008 | 2.088 | 0.008 | See |
| 10 | KMeans_tall | predict | 0.001 | 0.000 | 1000000 | 1000 | 100 | full | random | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.002 | 0.000 | 0.000 | 2.240 | 1.018 | See |
| 11 | KMeans_tall | predict | 0.000 | 0.000 | 1000000 | 1 | 100 | full | random | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.002 | 0.000 | 0.000 | 1.884 | 1.294 | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 300 |
| max_iter | 30 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | algorithm | init | max_iter | n_clusters | n_init | tol | n_iter_sklearn | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 0.341 | 0.013 | 10000 | 10000 | 2 | full | k-means++ | 30 | 300 | 1 | 0.0 | 30 | 0.003 | 30 | 0.005 | 0.110 | 0.003 | 3.103 | 0.146 | See |
| 1 | KMeans_short | predict | 0.001 | 0.000 | 10000 | 1000 | 2 | full | k-means++ | 30 | 300 | 1 | 0.0 | 30 | 0.003 | 30 | 0.005 | 0.001 | 0.000 | 1.164 | 0.439 | See |
| 2 | KMeans_short | predict | 0.000 | 0.000 | 10000 | 1 | 2 | full | k-means++ | 30 | 300 | 1 | 0.0 | 30 | 0.003 | 30 | 0.005 | 0.000 | 0.000 | 2.185 | 1.450 | See |
| 3 | KMeans_short | fit | 0.129 | 0.001 | 10000 | 10000 | 2 | full | random | 30 | 300 | 1 | 0.0 | 30 | 0.003 | 30 | 0.005 | 0.049 | 0.001 | 2.619 | 0.052 | See |
| 4 | KMeans_short | predict | 0.001 | 0.000 | 10000 | 1000 | 2 | full | random | 30 | 300 | 1 | 0.0 | 30 | 0.003 | 30 | 0.005 | 0.001 | 0.000 | 1.131 | 0.433 | See |
| 5 | KMeans_short | predict | 0.000 | 0.000 | 10000 | 1 | 2 | full | random | 30 | 300 | 1 | 0.0 | 30 | 0.003 | 30 | 0.005 | 0.000 | 0.000 | 2.307 | 1.725 | See |
| 6 | KMeans_short | fit | 0.829 | 0.029 | 10000 | 10000 | 100 | full | k-means++ | 30 | 300 | 1 | 0.0 | 16 | 0.003 | 17 | 0.005 | 0.373 | 0.024 | 2.226 | 0.161 | See |
| 7 | KMeans_short | predict | 0.003 | 0.002 | 10000 | 1000 | 100 | full | k-means++ | 30 | 300 | 1 | 0.0 | 16 | 0.003 | 17 | 0.005 | 0.001 | 0.000 | 1.998 | 1.387 | See |
| 8 | KMeans_short | predict | 0.000 | 0.000 | 10000 | 1 | 100 | full | k-means++ | 30 | 300 | 1 | 0.0 | 16 | 0.003 | 17 | 0.005 | 0.000 | 0.000 | 1.708 | 1.062 | See |
| 9 | KMeans_short | fit | 0.246 | 0.018 | 10000 | 10000 | 100 | full | random | 30 | 300 | 1 | 0.0 | 25 | 0.003 | 20 | 0.005 | 0.167 | 0.020 | 1.467 | 0.209 | See |
| 10 | KMeans_short | predict | 0.003 | 0.001 | 10000 | 1000 | 100 | full | random | 30 | 300 | 1 | 0.0 | 25 | 0.003 | 20 | 0.005 | 0.001 | 0.000 | 2.225 | 1.141 | See |
| 11 | KMeans_short | predict | 0.000 | 0.000 | 10000 | 1 | 100 | full | random | 30 | 300 | 1 | 0.0 | 25 | 0.003 | 20 | 0.005 | 0.000 | 0.000 | 1.699 | 0.969 | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| penalty | l2 |
| dual | False |
| tol | 0.0001 |
| C | 1.0 |
| fit_intercept | True |
| intercept_scaling | 1 |
| class_weight | NaN |
| random_state | NaN |
| solver | lbfgs |
| max_iter | 100 |
| multi_class | auto |
| verbose | 0 |
| warm_start | False |
| n_jobs | NaN |
| l1_ratio | NaN |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | C | class_weight | dual | fit_intercept | intercept_scaling | l1_ratio | max_iter | multi_class | n_jobs | penalty | random_state | solver | tol | verbose | warm_start | n_iter | accuracy_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 11.247 | 0.014 | 1000000 | 1000000 | 100 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0 | 0 | False | [20] | 0.2 | 11.287 | 0.050 | 0.996 | 0.005 | See |
| 1 | LogisticRegression | predict | 0.000 | 0.000 | 1000000 | 1000 | 100 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0 | 0 | False | [20] | 0.2 | 0.000 | 0.000 | 0.815 | 0.496 | See |
| 2 | LogisticRegression | predict | 0.000 | 0.000 | 1000000 | 1 | 100 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0 | 0 | False | [20] | 0.2 | 0.000 | 0.000 | 0.309 | 0.273 | See |
| 3 | LogisticRegression | fit | 0.795 | 0.012 | 1000 | 1000 | 10000 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0 | 0 | False | [28] | 0.2 | 0.772 | 0.020 | 1.030 | 0.030 | See |
| 4 | LogisticRegression | predict | 0.002 | 0.000 | 1000 | 100 | 10000 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0 | 0 | False | [28] | 0.2 | 0.003 | 0.001 | 0.546 | 0.139 | See |
| 5 | LogisticRegression | predict | 0.000 | 0.000 | 1000 | 1 | 10000 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0 | 0 | False | [28] | 0.2 | 0.001 | 0.000 | 0.123 | 0.077 | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| alpha | 1.0 |
| fit_intercept | True |
| normalize | False |
| copy_X | True |
| max_iter | NaN |
| tol | 0.001 |
| solver | auto |
| random_state | NaN |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | alpha | copy_X | fit_intercept | max_iter | normalize | random_state | solver | tol | n_iter | r2_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1.759 | 0.044 | 100000 | 100000 | 1000 | 1.0 | True | True | NaN | False | NaN | auto | 0.001 | NaN | 1.0 | 0.940 | 0.003 | 1.870 | 0.047 | See |
| 1 | Ridge | predict | 0.001 | 0.000 | 100000 | 1000 | 1000 | 1.0 | True | True | NaN | False | NaN | auto | 0.001 | NaN | 1.0 | 0.001 | 0.000 | 0.829 | 0.425 | See |
| 2 | Ridge | predict | 0.000 | 0.000 | 100000 | 1 | 1000 | 1.0 | True | True | NaN | False | NaN | auto | 0.001 | NaN | 1.0 | 0.000 | 0.000 | 0.633 | 0.647 | See |
| 3 | Ridge | fit | 1.173 | 0.015 | 1000000 | 1000000 | 100 | 1.0 | True | True | NaN | False | NaN | auto | 0.001 | NaN | 1.0 | 0.242 | 0.002 | 4.837 | 0.070 | See |
| 4 | Ridge | predict | 0.000 | 0.000 | 1000000 | 1000 | 100 | 1.0 | True | True | NaN | False | NaN | auto | 0.001 | NaN | 1.0 | 0.000 | 0.000 | 0.723 | 0.553 | See |
| 5 | Ridge | predict | 0.000 | 0.000 | 1000000 | 1 | 100 | 1.0 | True | True | NaN | False | NaN | auto | 0.001 | NaN | 1.0 | 0.000 | 0.000 | 0.616 | 0.642 | See |
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